Former Google CEO Eric Schmidt has slammed the US government for its slow 5G rollout, arguing that the government's "dithering" has left America "well behind" China. Dr Schmidt penned an op-ed in the Wall Street Journal alongside Harvard government professor Graham Allison, saying that the US is "far behind in almost every dimension of 5G while other nations – including China – race ahead". The authors said the Biden administration must make 5G a "national priority". If not, "China will own the 5G future", they said. "The step up to real 5G speeds will lead to analogous breakthroughs in autonomous vehicles, virtual-reality applications like the metaverse, and other areas that have yet to be invented," Dr Schmidt and Dr Allison wrote.

Session Leads are industry professionals who provide world-class technical support to our learners in their Nanodegree journey. All our session leads are handpicked through a rigorous selection process so that we can truly be #studentfirst. As a Session Lead/Instructor, you will be meeting virtually on a weekly basis with your students for group work, activities and short lectures. During the week, students are expected to study an estimated 10 hours on their Nanodegree programs on their own, and you will be expected to respond to their technical questions and check-in with them to make sure they are making progress. This program runs in partnership with an organization with an aim to enrol 10,000 learners in Africa!

It may sound obvious, perhaps even clichéd, but this mantra is something that must be remembered in ongoing political negotiations over Horizon Europe, which could see Switzerland and the UK excluded from EU research projects. We need more, not fewer, researchers collaborating to solve today's and tomorrow's challenges. By closely working with Swiss and British researchers, who have long played key roles, Horizon Europe projects will benefit – as they have in the past. This is the motivation behind ETH Zurich, which collaborates with IBM Research on nanotechnology, leading the Stick to Science campaign. This calls on all three parties – Switzerland, the UK and the EU – to try and solve the current stalemate and put Swiss and British association agreements in place.

PySpark is an interface for Apache Spark in Python. PySpark is often used for large-scale data processing and machine learning. We just released a PySpark crash course on the freeCodeCamp.org Krish is a lead data scientist and he runs a popular YouTube channel. Apache Spark is written in the Scala programming language. To support Python with Spark, the Apache Spark community released a tool called PySpark. PySpark allows people to work with Resilient Distributed Datasets (RDDs) in Python through a library called Py4j. PiSpark is an interface for Apache Spark in Python is often used for large scale data processing and machine learning. Krish knack teaches this course. So we are going to start Apache Spark series. And specifically, if I talk about Spark, we will be focusing on how we can use spark with Python. So we are going to discuss about the library called pi Spark, we will try to understand everything why spark is actually required. And probably will also try to cover a lot of ...

We may think of Artificial Intelligence as some obscure, futuristic concept of the Fourth Industrial Revolution that is associated with scenes from "out-there" sci-fi movies like Star Wars and Terminator. But AI is already here and being used in a wide variety of technological developments that make our daily responsibilities more efficient and convenient. In fact, scientists believe we are already'cyborgs' because we already have a'digital self', which lives into perpetuity. Artificial intelligence (AI) is intelligence demonstrated by machines and contrasts with natural intelligence displayed by humans. Towards Data Science says "Artificial Intelligence is the ability of a computer program to learn and think. Everything can be considered Artificial intelligence if it involves a program doing something that we would normally think would rely on the intelligence of a human."

AI thought leader Aruna Pattam discusses the wide-ranging applications of data science in society and the importance of diversity for the future of the industry. Data science draws on a large array of skills and expertise within the tech sphere. It uses statistics and mathematics, analytical modelling, machine learning and AI to analyse data and extract insights. Accenture's Shane O'Neill said those who pursue a career in data science can potentially work in any industry that uses data, which nowadays is pretty much every industry. But while we know that data science is important, you might not be aware of just how many areas of society it touches and how it can be used to solve a broad range of real-world challenges.

2021 was another bumper year for fines slapped against financial institutions (FIs) for failures in anti-money laundering (AML) compliance. AML shortcomings in transaction monitoring are a global problem. Countries whose banks were hit with fines include the United States, Germany, the Netherlands, Norway, Latvia, France, the UAE, India, Malaysia, and South Africa.  Fines imposed on FIs by regulators could reach as high as $2 billion for a second year running when the final figures come in, according to estimates. The continuous vigilance of regulators should serve as a wake-up call for financial institutions worldwide to take stock in failures and take action to change the trend in 2022. Some guilty parties lacked an AML compliance culture or even engaged in outright fraud and corruption. Others turned a blind eye. For FIs investing in large and costly compliance teams and tools, it’s surely frustrating to be hit with fines of tens

In 2020, IDRC and the Swedish International Development Cooperation Agency (Sida) launched the Artificial Intelligence for Development in Africa (IAPD Africa) program. This program aims to support the AI community and policymakers in developing responsible, ethical, and equitable AI that meets the continent's challenges, under the leadership of Africa. IDRC, the International Development Research Centre, was established in Canada in 1970 with a mission "to initiate, encourage, support and conduct research into the problems of the developing regions of the world and into the application of scientific, technical and other knowledge for the economic and social advancement of those regions . IDRC sees climate change and inequality, combined with the HIV/AIDS pandemic, as major obstacles to achieving the UN's sustainable development goals, and it is these challenges that it helps to address. While the center is headquartered in Ottawa, Canada, its five regional offices are located in India, Jordan, Kenya, Senegal, and Uruguay to be as close as possible to the researchers and projects it funds.

The theory of technological uniqueness is that in a quarter of a century, non-biological intelligence will match the field and accuracy of human intelligence. It will then outperform it due to the continuous acceleration of information technology, as well as the ability of machines to share their knowledge immediately, immediately followed by the ability of the machine to (perceive) or (consciously) and reach a level of intelligence similar to that of humans and superior to that it evolves much faster than it. Now, this was the definition of a theory in general that has a lot of endless branches, but the most associated personality with this concept now is Ray Kurzweil. Ray is a missionary, so to speak, of the concept of technological singularity and always tries to make his predictions based on solid scientific basis (as we will see later). Ray has a collection of books, lectures, films and even conferences and universities that discuss and prepare for this uniqueness.

There is a growing interest in dataset generation recently due to the superior generative capacity of large pre-trained language models (PLMs). In this paper, we study a flexible and efficient zero-short learning method, ZeroGen. Given a zero-shot task, we first generate a dataset from scratch using PLMs in an unsupervised manner. Then, we train a tiny task model (e.g., LSTM) under the supervision of the synthesized dataset. This approach allows highly efficient inference as the final task model only has orders of magnitude fewer parameters comparing to PLMs (e.g., GPT2-XL). Apart from being annotation-free and efficient, we argue that ZeroGen can also provide useful insights from the perspective of data-free model-agnostic knowledge distillation, and unreferenced text generation evaluation. Experiments and analysis on different NLP tasks, namely, text classification, question answering, and natural language inference), show the effectiveness of ZeroGen.